Test Case Prioritization Using Extended Digraphs
Why this work is in the frame
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Bibliographic record
Abstract
Although many test case prioritization techniques exist, their performance is far from perfect. Hence, we propose a new fault-based test case prioritization technique to promote fault-revealing test cases in model-based testing (MBT) procedures. We seek to improve the fault detection rate—a measure of how fast a test suite is able to detect faults during testing—in scenarios such as regression testing. We propose an extended digraph model as the basis of this new technique. The model is realized using a novel reinforcement-learning (RL)- and hidden-Markov-model (HMM)-based technique which is able to prioritize test cases for regression testing objectives. We present a method to initialize and train an HMM based upon RL concepts applied to an application's digraph model. The model prioritizes test cases based upon forward probabilities, a new test case prioritization approach. In addition, we also propose an alternative approach to prioritizing test cases according to the amount of change they cause in applications. To evaluate the effectiveness of the proposed techniques, we perform experiments on graphical user interface (GUI)-based applications and compare the results with state-of-the-art test case prioritization approaches. The experimental results show that the proposed technique is able to detect faults early within test runs.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it